Thermodynamic and Cost Analysis of a Solar Dish Power Plant in Spain Hybridized with a Micro-Gas Turbine

[EN]Small-scale hybrid parabolic dish concentrated solar power systems are a promising option to obtain distributed electricity. During the day, solar energy is used to produce electricity, and the absence of sunlight can be overwhelmed with fuel combustion. This study presents a thermo-economic survey for a hybridized power plant in different regions of Spain, considering the local climatic conditions. The developed model considers the instant solar irradiance and ambient temperature dynamically, providing an estimation of the power output, the associated fuel consumption, and the most relevant pollutant emissions linked to combustion. Hybrid and combustion-only operating modes at selected geographical locations in Spain (with different latitudes, mean solar irradiances, and meteorological conditions) are analyzed. The levelized cost of electricity indicator is estimated as a function of investment, interest rate, maintenance, and fuel consumption actual costs in Spain. Values of about 124 e/MWhe are feasible. Fuel consumption and emissions in hybrid operation can be reduced above 30% with respect to those of the same turbine working in a pure combustion mode. This model shows the potential of hybrid solar dishes to become cost-competitive against non-renewable technologies from the point of view of costs and reduction in gas emission levels in regions with high solar radiation and low water resources.University of Salamanca Grant Number PC-TCUE18-20-002, Junta de Castilla y León of Spain Grant Number SA017P17

A framework-based wind forecasting to assess wind potential with improved grey wolf optimization and support vector regression

Wind energy is one of the most promising alternates of fossil fuels because of its abundant availability, low cost, and pollution-free attributes. Wind potential estimation, wind forecasting, and effective wind-energy management are the critical factors in planning and managing wind farms connected to wind-pooling substations. Hence, this study proposes a hybrid framework-based approach for wind-resource estimation and forecasting, namely IGWO-SVR (improved grey wolf optimization method (IGWO)-support vector regression (SVR)) for a real-time power pooling substation. The wind resource assessment and behavioral wind analysis has been carried out with the proposed IGWO-SVR optimization method for hourly, daily, monthly, and annual cases using 40 years of ERA (European Center for Medium-Range Weather Forecast reanalysis) data along with the impact of the El Niño effect. First, wind reassessment is carried out considering the impact of El Niño, wind speed, power, pressure, and temperature of the selected site Radhapuram substation in Tamilnadu, India and reported extensively. In addition, statistical analysis and wind distribution fitting are performed to demonstrate the seasonal effect. Then the proposed model is adopted for wind speed forecasting based on the dataset. From the results, the proposed model offered the best assessment report and predicted the wind behavior with greater accuracy using evaluation metrics, namely root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). For short-term wind speed, power, and El Niño forecasting, IGWO-SVR optimization effectively outperforms other existing models. This method can be adapted effectively in any potential locations for wind resource assessment and forecasting needs for better renewable energy management by power utilities

Cerberus: A Deep Learning Hybrid Model for Lithium-Ion Battery Aging Estimation and Prediction Based on Relaxation Voltage Curves

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices, encompassing aspects such as performance delivery and cycling utilization. Consequently, the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention. Nonetheless, prevailing research predominantly concentrates on either aging estimation or prediction, neglecting the dynamic fusion of both facets. This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning, wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes. By amalgamating historical capacity decay data, the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries. Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates. Specifically, under a charging condition of 0.25C, a mean absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems (BMS), thereby affording estimations and prognostications of capacity decline with heightened precision.Comment: 3 figures, 1 table, 9 page

Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation

Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P₁ and 0.04P₁, respectively, where P₁ is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters

Optimal Power Allocation for Channel Estimation in MIMO-OFDM System with Per-Subcarrier Transmit Antenna Selection

A novel hybrid channel estimator is proposed for multiple-input multiple-output orthogonal frequency- division multiplexing (MIMO-OFDM) system with per-subcarrier transmit antenna selection having optimal power allocation among subcarriers. In practice, antenna selection information is transmitted through a binary symmetric control channel with a crossover probability. Linear minimum mean-square error (LMMSE) technique is optimal technique for channel estimation in MIMO-OFDM system. Though LMMSE estimator performs well at low signal to noise ratio (SNR), in the presence of antenna-to-subcarrier-assignment error (ATSA), it introduces irreducible error at high SNR. We have proved that relaxed MMSE (RMMSE) estimator overcomes the performance degradation at high SNR. The proposed hybrid estimator combines the benefits of LMMSE at low SNR and RMMSE estimator at high SNR. The vector mean square error (MSE) expression is modified as scalar expression so that an optimal power allocation can be performed. The convex optimization problem is formulated and solved to allocate optimal power to subcarriers minimizing the MSE, subject to transmit sum power constraint. Further, an analytical expression for SNR threshold at which the hybrid estimator is to be switched from LMMSE to RMMSE is derived. The simulation results show that the proposed hybrid estimator gives robust performance, irrespective of ATSA error